Answer :
We reject the null hypothesis and conclude that the true average temperature of COVID patients exceeds 100, with a type I error rate of 10% and a p-value of 0.0068 indicating a low probability of obtaining the observed sample mean if the null hypothesis were true.
To test if the true average temperature of COVID patients exceeds 100, we can use a one-sample z-test.
The null and alternative hypotheses are
Null hypothesis: The true average temperature of COVID patients is less than or equal to 100.
Alternative hypothesis: The true average temperature of COVID patients exceeds 100.
We can calculate the test statistic as
z = (x - μ) / (σ / sqrt(n))
where x is the sample mean, μ is the hypothesized population mean (100 in this case), σ is the population standard deviation, and n is the sample size.
Substituting the given values, we get
z = (101.2 - 100) / (6 / sqrt(56))
z = 2.47
We can find that the p-value is 0.0068. This means that if the true average temperature of COVID patients is actually 100, there is only a 0.68% chance of getting a sample mean of 101.2 or higher.
Since the alpha level is 10%, and the p-value is less than 10%, we reject the null hypothesis and conclude that the true average temperature of COVID patients exceeds 100.
The type of error that could have occurred is a type I error, which is rejecting the null hypothesis when it is actually true. The probability of a type I error is equal to the chosen alpha level, which is 10% in this case.
Learn more about one-sample z-test here
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